Abstract
The weighted least squares design of FIR filters is implemented in terms of a feedback neural network. The proposed neural network is shown to converge to the global minimum in each iteration for the current weighting function, and as the weighting function is adjusted from iteration to iteration, an equiripple design is achieved. The approach is applicable to FIR filters with piecewise-constant amplitude responses, as well as to digital differentiators and Hilbert transformers. The proposed configuration is amenable to analog very-large-scale integration and can, therefore, be used in real-time signal processing.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
